FIP 0014 thematic notebook

The goal of this notebook is to explore the impact of the upcoming sector expiration wave on April / May, especially when considering the ones with V1 proofs.

Setup and dependences

%load_ext autotime

%load_ext autoreload

%autoreload 2

time: 7.86 ms (started: 2021-08-15 00:11:27 +00:00)

# External dependences
import pandas as pd
import numpy as np
import plotly.express as px
from prophet import Prophet
import matplotlib.pyplot as plt

# Move path to parent folder
import sys
sys.path.insert(1, '../')

import plotly
plotly.offline.init_notebook_mode()

time: 885 ms (started: 2021-08-15 00:11:27 +00:00)

# Create a connection object from a conn string
from filecoin_metrics.connection import get_connection, get_connection_string
conn_string = get_connection_string('../config/sentinel-conn-string.txt')
connection = get_connection(conn_string)

time: 1.45 s (started: 2021-08-15 00:11:28 +00:00)

QUERY = """
/* Get the last state of the sectors */
with sector_states as ( 
	select 
	msi.*,
	max(msi.height) over (partition by msi.sector_id, msi.miner_id) as max_height
	from miner_sector_infos msi 
	where msi.activation_epoch > 0
	and msi.expiration_epoch > msi.height /* Get only active sectors */
	order by max_height 
)
select 
count(*) as sector_count,
sum(ss.initial_pledge::numeric) / 1e18 as initial_pledge_in_fil,
count(*) * 32 as network_power_in_gb,
date_trunc('DAY', to_timestamp(height_to_unix(ss.activation_epoch))) as activation_date,
date_trunc('DAY', to_timestamp(height_to_unix(ss.expiration_epoch))) as expiration_date
from sector_states as ss
where ss.max_height = ss.height /* get the last state of the info */
group by activation_date, expiration_date
order by activation_date, expiration_date
"""

query_df = (pd.read_sql(QUERY, connection)
              .assign(network_power_in_pib=lambda df: df.network_power_in_gb / (1024 ** 2))
              .assign(initial_pledge_in_thousand_fil=lambda df: df.initial_pledge_in_fil / 1000))

time: 4min 40s (started: 2021-08-15 00:11:30 +00:00)

# Maximum date for V1 sectors
UPGRADE_DATE = '2020-11-25 00:00:00'

metrics = {'is_v1': lambda x:  x['activation_date'] < UPGRADE_DATE}

query_df = query_df.assign(**metrics)

time: 16.2 ms (started: 2021-08-15 00:16:10 +00:00)

def resample_and_bar_plot(df, resample_rule, time_column, value_column, title, **kwargs):
    fig_df = df.resample(resample_rule, on=time_column, label='left').sum()
    fig = px.bar(fig_df.reset_index(),
                 x=time_column,
                 y=value_column,
                 title=title,
                 **kwargs)
    return fig


def resample_and_bar_plot_relative(df, resample_rule, time_column, value_column, title, **kwargs):
    fig_df = df.resample(resample_rule, on=time_column, label='left').sum()
    y = fig_df.groupby(time_column).sum()
    fig_df /= y
    fig = px.bar(fig_df.reset_index(),
                 x=time_column,
                 y=value_column,
                 title=title,
                 **kwargs)
    return fig

time: 13.2 ms (started: 2021-08-15 00:16:10 +00:00)

Sector Count

df = query_df.copy()

print("Basic stats")
print("---")
print(f"Total sectors (#): {df.sector_count.sum()}")
print(f"Raw bytes power (PiB): {df.network_power_in_gb.sum() / (1024 ** 2) :.3g}")
print(f"Initial pledge (FIL): {df.initial_pledge_in_fil.sum()}")
print("---")

Basic stats
---
Total sectors (#): 75376301
Raw bytes power (PiB): 2.3e+03
Initial pledge (FIL): 22521296.37945206
---
time: 14.1 ms (started: 2021-08-15 00:16:11 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Count of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 369 ms (started: 2021-08-15 00:16:11 +00:00)

resample_rule = '1d'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Count of Expiring  Sectors Before 15Jun2021 (log #)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']



fig_df = (df.query("expiration_date < '2021-06-15 00:00+00:00'")
            .groupby(groups)
            .sum()
            .reset_index()
         )

if fig_df.empty:
   print('WARNING: DataFrame is Empty')
else:
   fig = px.bar(fig_df,
               x=time_column,
               y=value_column,
               facet_row=fig_df.is_v1,
               title=title,
               log_y=True)
   fig.show()

WARNING: DataFrame is Empty
time: 29.2 ms (started: 2021-08-15 00:16:11 +00:00)

sector_count = df.sector_count.sum()
v1_count = df.query("is_v1 == True").sector_count.sum()

v1_6mo_count = (df.query("is_v1 == True & expiration_date < '2021-06-01 00:00+00:00'")
                           .sector_count
                           .sum())

v1_12mo_count = (df.query("is_v1 == True & expiration_date < '2021-12-01 00:00+00:00' & expiration_date >= '2021-06-01 00:00+00:00'")
                           .sector_count
                           .sum())


v1_18mo_count = (df.query("is_v1 == True & expiration_date < '2022-06-01 00:00+00:00' & expiration_date >= '2021-12-01 00:00+00:00'")
                           .sector_count
                           .sum())

print("---")
print(f"Total sectors (#): {sector_count}")
print(f"V1 sectors (#): {v1_count}")
print(f"V1 sectors share (%): {v1_count / sector_count :.1%}")
print("---")
print(f"6mo V1 sectors share (%) of total sectors: {v1_6mo_count / sector_count :.2%}")
print(f"12mo V1 sectors share (%) of total sectors: {v1_12mo_count / sector_count :.2%}")
print(f"18mo V1 sectors share (%) of total sectors: {v1_18mo_count / sector_count :.2%}")
print("---")
print(f"6mo V1 sectors share (%) of total sectors: {v1_6mo_count / v1_count :.2%}")
print(f"12mo V1 sectors share (%) of total sectors: {v1_12mo_count / v1_count :.2%}")
print(f"18mo V1 sectors share (%) of total sectors: {v1_18mo_count / v1_count :.2%}")
print("---")

---
Total sectors (#): 75376301
V1 sectors (#): 1189468
V1 sectors share (%): 1.6%
---
6mo V1 sectors share (%) of total sectors: 0.00%
12mo V1 sectors share (%) of total sectors: 0.00%
18mo V1 sectors share (%) of total sectors: 1.58%
---
6mo V1 sectors share (%) of total sectors: 0.00%
12mo V1 sectors share (%) of total sectors: 0.00%
18mo V1 sectors share (%) of total sectors: 100.00%
---
time: 32.7 ms (started: 2021-08-15 00:16:11 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'sector_count'
title = 'Upcoming Sector Expiration Count, grouped by sector version (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             facet_row='is_v1',
             title=title)
fig.show()

time: 97 ms (started: 2021-08-15 00:16:11 +00:00)

resample_rule = '1w'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Activated Sector Count, grouped by sector version (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )

fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             facet_row='is_v1',
             title=title)
fig.show()

time: 101 ms (started: 2021-08-15 00:16:11 +00:00)

Forecasts

rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None))
             .assign(y=lambda df: df.y.cumsum() / (32 * 1024 * 1024)))

m = Prophet(changepoint_prior_scale=0.4)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted RB Storage Power')
plt.xlabel("Time")
plt.ylabel("Total RB Storage Power (EiB)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -35.0067
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       1139.31     0.0776257       508.746           1           1      123   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     199       1225.68    0.00508029       309.335           1           1      232   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       1238.37   0.000379716       298.079           1           1      343   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     399       1262.26     0.0224918       135.873           1           1      458   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     499       1267.26     0.0081595       107.049           1           1      576   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     599       1270.63   0.000196035       73.6143           1           1      683   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     699       1273.44    0.00120385       70.3905           1           1      800   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     799       1277.43     0.0194561       254.225           1           1      913   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     899       1280.91   0.000101534       34.8788           1           1     1023   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     955        1281.3   4.66146e-05       12.3742   2.074e-06       0.001     1129  LS failed, Hessian reset 
     999       1281.49    0.00228042       59.0196      0.8704      0.8704     1180   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1099        1281.9   0.000962236       32.5382           1           1     1300   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1199       1282.11   0.000211426        64.755      0.3577      0.3577     1421   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1299        1282.5     0.0338419        69.001           1           1     1536   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1350       1282.63   5.84207e-06       13.6061   4.664e-07       0.001     1642  LS failed, Hessian reset 
    1371       1282.64   9.25564e-05       26.4229    1.04e-05       0.001     1718  LS failed, Hessian reset 
    1380       1282.65   4.07281e-06       9.32619   3.153e-07       0.001     1767  LS failed, Hessian reset 
    1399       1282.66   1.65302e-05       20.2031      0.7108      0.7108     1790   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1499       1282.72   0.000106485       22.9923      0.3017           1     1921   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1599       1282.76   1.03062e-05       22.9483   4.267e-07       0.001     2085  LS failed, Hessian reset 
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1660       1282.77   8.98534e-06       18.7586   7.626e-07       0.001     2210  LS failed, Hessian reset 
    1699       1282.77   0.000284016       9.95813           1           1     2257   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1799       1283.05     0.0144849       84.4298           1           1     2367   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1899       1283.14   0.000982855       40.9433           1           1     2487   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    1937       1283.15   2.86309e-05       10.0694   2.102e-06       0.001     2579  LS failed, Hessian reset 
    1999       1283.15   0.000181262       9.03113           1           1     2662   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
    2022       1283.15   4.73533e-06       10.6747   3.908e-07       0.001     2730  LS failed, Hessian reset 
    2039       1283.15   3.41385e-07       5.38306      0.2688           1     2752   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 3.41 s (started: 2021-08-15 00:16:11 +00:00)

rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None)))

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Daily New Sectors')
plt.xlabel("Time")
plt.ylabel("Daily New Sectors (#)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -10.9238
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      99       549.154     0.0407864       65.4551           1           1      118   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     194       555.847   0.000701584       24.9714   6.201e-06       0.001      270  LS failed, Hessian reset 
     199       556.401    0.00800813       23.3793           1           1      276   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       557.626     0.0933311       111.273      0.5942      0.5942      396   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     380       558.144   0.000464055       39.9562   1.644e-05       0.001      538  LS failed, Hessian reset 
     399       558.153   2.64097e-06        19.933      0.1578      0.6252      564   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     428       558.161   0.000139582       15.9692   5.699e-06       0.001      633  LS failed, Hessian reset 
     499       558.178   2.55309e-05       19.4393      0.5963      0.5963      722   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     599       558.776    0.00206134       26.1339           1           1      843   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     668       559.013   0.000937077        25.076   4.824e-05       0.001      957  LS failed, Hessian reset 
     699       559.068   0.000163952       18.3926      0.3772           1      989   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     767       559.077   3.50342e-06       21.1724    1.65e-07       0.001     1120  LS failed, Hessian reset 
     785       559.077   9.33083e-08       17.8457      0.3309           1     1145   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 2.13 s (started: 2021-08-15 00:16:15 +00:00)

rename_cols = {'activation_date': 'ds',
               'sector_count': 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00' & activation_date < '2021-03-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .sector_count
             .sum()
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None)))

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=90)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Daily New Sectors without March data')
plt.xlabel("Time")
plt.ylabel("Daily New Sectors (#)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -4.476
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      72       151.528    0.00255944       21.4481   7.658e-05       0.001      120  LS failed, Hessian reset 
      99       151.713   0.000149277       17.1013       2.201      0.2201      163   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     144       151.717   0.000179243       13.4875   8.639e-06       0.001      264  LS failed, Hessian reset 
     199       151.718   9.43985e-06       17.6273           1           1      342   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     226       151.733   5.13839e-05       17.9329   3.145e-06       0.001      416  LS failed, Hessian reset 
     252       151.733   9.53697e-09       15.6199     0.06896     0.06896      461   
Optimization terminated normally: 
  Convergence detected: absolute parameter change was below tolerance

time: 2.2 s (started: 2021-08-15 00:16:17 +00:00)

rename_cols = {'activation_date': 'ds',
               0: 'y'}

proj_df = (df.query("activation_date > '2020-11-01 00:00+00:00'")
             .resample("1d", on="activation_date")
             .apply(lambda x: (x.initial_pledge_in_fil / x.sector_count).mean())
             .reset_index()
             .rename(columns=rename_cols)
             .assign(ds=lambda df: df.ds.dt.tz_localize(None))
          )

m = Prophet(changepoint_prior_scale=0.2)
m.fit(proj_df)
future = m.make_future_dataframe(periods=60)
forecast = m.predict(future)

fig = m.plot(forecast, figsize=(10, 4))
plt.title('Past and Forecasted Initial Pledge per Sector')
plt.xlabel("Time")
plt.ylabel("Mean Initial Pledge per Sector (FIL)")
plt.show()

INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

Initial log joint probability = -4.78487
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
      65       327.656    0.00252298       45.1024   0.0001365       0.001      114  LS failed, Hessian reset 
      99       328.965    0.00355851       19.5719      0.4846     0.04846      152   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     130       329.659    0.00115403       37.5926   4.048e-05       0.001      222  LS failed, Hessian reset 
     199       330.489   0.000569071       19.1283           1           1      314   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     299       330.647    0.00112969       15.5719      0.7325      0.7325      442   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     399       332.004     0.0296414       20.2757           1           1      562   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     408       332.203    0.00103403       33.3078   5.548e-05       0.001      603  LS failed, Hessian reset 
     499       332.567   0.000583614       14.6775      0.4051           1      717   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     599       332.795   2.52175e-06       14.3895      0.8679      0.8679      847   
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes 
     615       332.796   5.62906e-08       13.4072      0.1989           1      868   
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance

time: 2.17 s (started: 2021-08-15 00:16:19 +00:00)

m.fit

<bound method Prophet.fit of <prophet.forecaster.Prophet object at 0x7f2bc5776ee0>>

time: 23.4 ms (started: 2021-08-15 00:16:21 +00:00)

resample_rule = '7d'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Count of Sector Activation Date (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          pd.Grouper(key='expiration_date', freq=resample_rule)]
fig_df = df.groupby(groups).sum().reset_index()
px.density_heatmap(fig_df,
                   x='activation_date',
                   y='expiration_date',
                   z='sector_count')

time: 121 ms (started: 2021-08-15 00:16:21 +00:00)

resample_rule = '1d'
time_column = 'activation_date'
value_column = 'sector_count'
title = 'Count of Sector Activation Date (#)'


groups = [pd.Grouper(key='activation_date', freq=resample_rule),
          pd.Grouper(key='expiration_date', freq=resample_rule)]
fig_df = df.groupby(groups).sum().reset_index()
fig = px.density_contour(fig_df,
                   x='activation_date',
                   y='expiration_date',
                   z='sector_count',
                   histfunc='sum')
fig.show()

time: 421 ms (started: 2021-08-15 00:16:21 +00:00)

Initial Pledge

sector_count = df.initial_pledge_in_fil.sum()
v1_count = df.query("is_v1 == True").initial_pledge_in_fil.sum()

v1_6mo_count = (df.query("is_v1 == True & expiration_date < '2021-06-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())

v1_12mo_count = (df.query("is_v1 == True & expiration_date < '2021-12-01 00:00+00:00' & expiration_date >= '2021-06-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())


v1_18mo_count = (df.query("is_v1 == True & expiration_date < '2022-06-01 00:00+00:00' & expiration_date >= '2021-12-01 00:00+00:00'")
                           .initial_pledge_in_fil
                           .sum())

print("---")
print(f"Total collateral (Million FIL): {sector_count / 1e6 :.3g}")
print(f"V1 collateral (Million FIL): {v1_count / 1e6 :.3g}")
print(f"V1 sectors share (%): {v1_count / sector_count :.1%}")
print("---")
print(f"6mo V1 sectors share (%) of total collateral: {v1_6mo_count / sector_count :.2%}")
print(f"12mo V1 sectors share (%) of total collateral: {v1_12mo_count / sector_count :.2%}")
print(f"18mo V1 sectors share (%) of total collateral: {v1_18mo_count / sector_count :.2%}")
print("---")
print(f"6mo V1 sectors share (%) of total collateral: {v1_6mo_count / v1_count :.2%}")
print(f"12mo V1 sectors share (%) of total collateral: {v1_12mo_count / v1_count :.2%}")
print(f"18mo V1 sectors share (%) of total collateral: {v1_18mo_count / v1_count :.2%}")
print("---")

---
Total collateral (Million FIL): 22.5
V1 collateral (Million FIL): 0.376
V1 sectors share (%): 1.7%
---
6mo V1 sectors share (%) of total collateral: 0.00%
12mo V1 sectors share (%) of total collateral: 0.00%
18mo V1 sectors share (%) of total collateral: 1.67%
---
6mo V1 sectors share (%) of total collateral: 0.00%
12mo V1 sectors share (%) of total collateral: 0.00%
18mo V1 sectors share (%) of total collateral: 100.00%
---
time: 36.4 ms (started: 2021-08-15 00:16:22 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'initial_pledge_in_fil'
title = 'Initial Pledge (FIL) of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )


fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 167 ms (started: 2021-08-15 00:16:22 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'initial_pledge_in_fil'
title = 'Initial Pledge (FIL) of Expiring Sectors, grouped by Sector Version'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )


fig = px.bar(fig_df,
             x=time_column,
             y=value_column,
             facet_col='is_v1',
             title=title)
fig.show()

time: 105 ms (started: 2021-08-15 00:16:22 +00:00)

resample_rule = '1m'
time_column = 'activation_date'
value_column = ['initial_pledge_in_thousand_fil']
title = 'Sum of Initial Pledge (FIL) across activation dates'
resample_and_bar_plot(df, resample_rule, time_column, value_column, title).show()

time: 87.4 ms (started: 2021-08-15 00:16:22 +00:00)

RB Network Power

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'network_power_in_pib'
title = 'RB Network Power (PiB) of Expiring  Sectors (#)'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
         )


fig = px.bar(fig_df.reset_index(),
             x=time_column,
             y=value_column,
             color='is_v1',
             title=title)
fig.show()

time: 95.1 ms (started: 2021-08-15 00:16:22 +00:00)

resample_rule = '1m'
time_column = 'expiration_date'
value_column = 'network_power_in_pib'
title = 'RB Network Power (PiB) of Expiring Sectors, grouped by Sector Version'


groups = [pd.Grouper(key='expiration_date', freq=resample_rule),
          'is_v1']

fig_df = (df.groupby(groups)
            .sum()
            .reset_index()
         )


fig = px.bar(fig_df,
             x=time_column,
             y=value_column,
             facet_col='is_v1',
             title=title)
fig.show()

time: 102 ms (started: 2021-08-15 00:16:22 +00:00)

resample_rule = '1m'
time_column = 'activation_date'
value_column = ['network_power_in_pib']
title = 'Sum of RB Network Power (PiB) across activation dates'
resample_and_bar_plot(df, resample_rule, time_column, value_column, title).show()

time: 79.3 ms (started: 2021-08-15 00:16:22 +00:00)